pacman::p_load(tmap, sf, DT, stplanr,
performance,
ggpubr, tidyverse)Hands-on Exercise 3: Processing and Visualising Flow Data
1 Overview
Spatial interaction describe quatitatively the flow of people, material, or information between locations in geographical space.
Conditions for Spatial Flows
Three interdependent conditions are necessary for a spatial interaction to occur:

Features

- Locations: A movement is occurring between a location of origin and a location of destination (i=origin; j =destination)
- Centroid: Abstraction of the attributes of a zone at a point
- Flows: Expressed by a valued vector Tij representing an interaction between locations i and j
- Vectors: A vector Tij links two centroids and has a value assigned to it (50) which can represents movements
1.1 Task
In this hands-on exercise, we will learn how to build an OD matrix by using Passenger Volume by Origin Destination Bus Stops data set downloaded from LTA DataMall. By the end of this hands-on exercise, we will be able:
- to import and extract OD data for a selected time interval,
- to import and save geospatial data (i.e. bus stops and mpsz) into sf tibble data frame objects,
- to populate planning subzone code into bus stops sf tibble data frame,
- to construct desire lines geospatial data from the OD data, and
- to visualise passenger volume by origin and destination bus stops by using the desire lines data.
1.2 Loading R Packages
- tmap for creating thematic maps; useful for static and interactive maps.
- sf for importing, integrating, processing and transforming geospatial data.
- DT for interactive data tables
- stplanr for sustainable transport planning; provides functions and tools for analysis and visualisation of transport projects
- performance for model performance measurement
- ggpubr for visualisation
- tidyverse for importing, integrating, wrangling and visualising data.
1.3 Preparing Flow Data
Note: Using October 2023 data because Postman API couldn’t find Oct 2022 data, maybe too long ago :(
odbus <- read_csv("data/aspatial/origin_destination_bus_202310.csv")glimpse(odbus)Rows: 5,694,297
Columns: 7
$ YEAR_MONTH <chr> "2023-10", "2023-10", "2023-10", "2023-10", "2023-…
$ DAY_TYPE <chr> "WEEKENDS/HOLIDAY", "WEEKDAY", "WEEKENDS/HOLIDAY",…
$ TIME_PER_HOUR <dbl> 16, 16, 14, 14, 17, 17, 17, 7, 14, 14, 10, 20, 20,…
$ PT_TYPE <chr> "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "…
$ ORIGIN_PT_CODE <chr> "04168", "04168", "80119", "80119", "44069", "2028…
$ DESTINATION_PT_CODE <chr> "10051", "10051", "90079", "90079", "17229", "2014…
$ TOTAL_TRIPS <dbl> 3, 5, 3, 5, 4, 1, 24, 2, 1, 7, 3, 2, 5, 1, 1, 1, 1…
odbus tibble data frame shows that the values in ORIGIN_PT_CODE and DESTINATON_PT_CODE are in numeric data type. Hence, the code chunk below is used to convert these data values into character data type.
odbus$ORIGIN_PT_CODE <- as.factor(odbus$ORIGIN_PT_CODE)
odbus$DESTINATION_PT_CODE <- as.factor(odbus$DESTINATION_PT_CODE) Recheck to confirm that the 2 variables have indeed been updated:
glimpse(odbus)Rows: 5,694,297
Columns: 7
$ YEAR_MONTH <chr> "2023-10", "2023-10", "2023-10", "2023-10", "2023-…
$ DAY_TYPE <chr> "WEEKENDS/HOLIDAY", "WEEKDAY", "WEEKENDS/HOLIDAY",…
$ TIME_PER_HOUR <dbl> 16, 16, 14, 14, 17, 17, 17, 7, 14, 14, 10, 20, 20,…
$ PT_TYPE <chr> "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "…
$ ORIGIN_PT_CODE <fct> 04168, 04168, 80119, 80119, 44069, 20281, 20281, 1…
$ DESTINATION_PT_CODE <fct> 10051, 10051, 90079, 90079, 17229, 20141, 20141, 1…
$ TOTAL_TRIPS <dbl> 3, 5, 3, 5, 4, 1, 24, 2, 1, 7, 3, 2, 5, 1, 1, 1, 1…
For our study, we will extract commuting flows on weekday and between 6 and 9 o’clock.
odbus6_9 <- odbus %>%
filter(DAY_TYPE == "WEEKDAY") %>%
filter(TIME_PER_HOUR >= 6 &
TIME_PER_HOUR <= 9) %>%
group_by(ORIGIN_PT_CODE,
DESTINATION_PT_CODE) %>%
summarise(TRIPS = sum(TOTAL_TRIPS))datatable allows for interactive tables:
Show the code
datatable(
odbus6_9,
filter='top')We will save the output in rds format for future use, and reimport the saved rds file into R environment:
write_rds(odbus6_9, "data/rds/odbus6_9.rds")
odbus6_9 <- read_rds("data/rds/odbus6_9.rds")1.4 Working with Geospatial Data
Point Data
busstop <- st_read(dsn = "data/geospatial",
layer = "BusStop") %>%
st_transform(crs = 3414)Reading layer `BusStop' from data source
`C:\kytjy\ISSS624\Hands-on_Ex\Hands-on_Ex3\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 5161 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21
Polygon data
mpsz <- st_read(dsn = "data/geospatial",
layer = "MPSZ-2019") %>%
st_transform(crs = 3414)Reading layer `MPSZ-2019' from data source
`C:\kytjy\ISSS624\Hands-on_Ex\Hands-on_Ex3\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS: WGS 84
mpszSimple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21 / Singapore TM
First 10 features:
SUBZONE_N SUBZONE_C PLN_AREA_N PLN_AREA_C REGION_N
1 MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
2 INSTITUTION HILL RVSZ05 RIVER VALLEY RV CENTRAL REGION
3 ROBERTSON QUAY SRSZ01 SINGAPORE RIVER SR CENTRAL REGION
4 JURONG ISLAND AND BUKOM WISZ01 WESTERN ISLANDS WI WEST REGION
5 FORT CANNING MUSZ02 MUSEUM MU CENTRAL REGION
6 MARINA EAST (MP) MPSZ05 MARINE PARADE MP CENTRAL REGION
7 SUDONG WISZ03 WESTERN ISLANDS WI WEST REGION
8 SEMAKAU WISZ02 WESTERN ISLANDS WI WEST REGION
9 SOUTHERN GROUP SISZ02 SOUTHERN ISLANDS SI CENTRAL REGION
10 SENTOSA SISZ01 SOUTHERN ISLANDS SI CENTRAL REGION
REGION_C geometry
1 CR MULTIPOLYGON (((33222.98 29...
2 CR MULTIPOLYGON (((28481.45 30...
3 CR MULTIPOLYGON (((28087.34 30...
4 WR MULTIPOLYGON (((14557.7 304...
5 CR MULTIPOLYGON (((29542.53 31...
6 CR MULTIPOLYGON (((35279.55 30...
7 WR MULTIPOLYGON (((15772.59 21...
8 WR MULTIPOLYGON (((19843.41 21...
9 CR MULTIPOLYGON (((30870.53 22...
10 CR MULTIPOLYGON (((26879.04 26...
Combine Busstop and mpsz
busstop_mpsz <- st_intersection(busstop, mpsz) %>%
select(BUS_STOP_N, SUBZONE_C) %>%
st_drop_geometry()Show the code
datatable(busstop_mpsz)Save the output in rds format for future use:
write_rds(busstop_mpsz, "data/rds/busstop_mpsz.rds") Append planning subzone code from busstop_mpsz onto odbus6_9
od_data <- left_join(odbus6_9 , busstop_mpsz,
by = c("ORIGIN_PT_CODE" = "BUS_STOP_N")) %>%
rename(ORIGIN_BS = ORIGIN_PT_CODE,
ORIGIN_SZ = SUBZONE_C,
DESTIN_BS = DESTINATION_PT_CODE)Duplicates Check
Check for duplicates to prevent double counting:
duplicate <- od_data %>%
group_by_all() %>%
filter(n()>1) %>%
ungroup()
duplicate# A tibble: 1,186 × 4
ORIGIN_BS DESTIN_BS TRIPS ORIGIN_SZ
<chr> <fct> <dbl> <chr>
1 11009 01341 1 QTSZ01
2 11009 01341 1 QTSZ01
3 11009 01411 4 QTSZ01
4 11009 01411 4 QTSZ01
5 11009 01421 17 QTSZ01
6 11009 01421 17 QTSZ01
7 11009 01511 19 QTSZ01
8 11009 01511 19 QTSZ01
9 11009 01521 2 QTSZ01
10 11009 01521 2 QTSZ01
# ℹ 1,176 more rows
If duplicated records are found, the code chunk below will be used to retain the unique records.
od_data <- unique(od_data)Update od_data with planning subzone codes
od_data <- left_join(od_data , busstop_mpsz,
by = c("DESTIN_BS" = "BUS_STOP_N")) duplicate <- od_data %>%
group_by_all() %>%
filter(n()>1) %>%
ungroup()
duplicate# A tibble: 1,350 × 5
ORIGIN_BS DESTIN_BS TRIPS ORIGIN_SZ SUBZONE_C
<chr> <chr> <dbl> <chr> <chr>
1 01013 51071 2 RCSZ10 CCSZ01
2 01013 51071 2 RCSZ10 CCSZ01
3 01112 51071 66 RCSZ10 CCSZ01
4 01112 51071 66 RCSZ10 CCSZ01
5 01112 53041 4 RCSZ10 BSSZ01
6 01112 53041 4 RCSZ10 BSSZ01
7 01121 51071 8 RCSZ04 CCSZ01
8 01121 51071 8 RCSZ04 CCSZ01
9 01121 82221 1 RCSZ04 GLSZ05
10 01121 82221 1 RCSZ04 GLSZ05
# ℹ 1,340 more rows
Retain unique records:
od_data <- unique(od_data)Aggregate Data
od_data <- od_data %>%
# Rename column for better clarity
rename(DESTIN_SZ = SUBZONE_C) %>%
# Remove NAs
drop_na() %>%
# Group and summarise number of trips at each O/D level
group_by(ORIGIN_SZ, DESTIN_SZ) %>%
summarise(MORNING_PEAK = sum(TRIPS))
od_data# A tibble: 21,079 × 3
# Groups: ORIGIN_SZ [310]
ORIGIN_SZ DESTIN_SZ MORNING_PEAK
<chr> <chr> <dbl>
1 AMSZ01 AMSZ01 2694
2 AMSZ01 AMSZ02 10591
3 AMSZ01 AMSZ03 14980
4 AMSZ01 AMSZ04 3106
5 AMSZ01 AMSZ05 7734
6 AMSZ01 AMSZ06 2306
7 AMSZ01 AMSZ07 1824
8 AMSZ01 AMSZ08 2734
9 AMSZ01 AMSZ09 2300
10 AMSZ01 AMSZ10 164
# ℹ 21,069 more rows
Save the output in rds format for future use, and reimport into R environment:
write_rds(od_data, "data/rds/od_data.rds")
od_data <- read_rds("data/rds/od_data.rds")1.5 Visualising Spatial Interaction
We will not plot the intra-zonal flows, i.e. where the origin and destination are the same (eg origin = AMSZ01 and destination = AMSZ01)
The code chunk below will be used to remove intra-zonal flows.
od_data1 <- od_data[od_data$ORIGIN_SZ!=od_data$DESTIN_SZ,]The comma , after the condition is significant. In R’s data frame syntax, the format for subsetting is [rows, columns]. When you place a condition before the comma, it applies to rows. The comma itself then implies that you’re not applying any specific filter to the columns – meaning you want all columns.
flowLine <- od2line(flow = od_data1,
zones = mpsz,
zone_code = "SUBZONE_C")tm_shape(mpsz) +
tm_polygons() +
flowLine %>%
tm_shape() +
tm_lines(lwd = "MORNING_PEAK",
style = "quantile",
scale = c(0.1, 1, 3, 5, 7, 10),
n = 6,
alpha = 0.3)
When the flow data are very messy and highly skewed like the one shown above, it is wiser to focus on selected flows, for example flow greater than or equal to 5000 as shown below.
tm_shape(mpsz) +
tm_polygons() +
flowLine %>%
filter(MORNING_PEAK >= 5000) %>%
tm_shape() +
tm_lines(lwd = "MORNING_PEAK",
style = "quantile",
scale = c(0.1, 1, 3, 5, 7, 10),
n = 6,
alpha = 0.3)